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Population Research and Policy Review

, Volume 33, Issue 4, pp 603–614 | Cite as

Research Note: US Census Same-Sex Couple Data: Adjustments to Reduce Measurement Error and Empirical Implications

  • Rebecca DiBennardoEmail author
  • Gary J. Gates
Article

Abstract

US Census same-sex couple data represent one of the richest and most frequently used data resources for studying the LGBT population. Recently, the Census Bureau conducted an analysis of a serious measurement problem in these data, finding that as many as 40 % of same-sex couples tabulated in Census 2000 and 28 % of those tabulated in Census 2010 were likely misclassified different-sex couples (O’Connell and Feliz, Bureau of the Census, 2011). As a result, the Census Bureau released new state-level “preferred” estimates for the number of same-sex couples in these years, as well as previously unavailable information regarding the error rate of sex misclassification among different-sex married and unmarried couples by state and year. Researchers can use this information to adjust same-sex couple tabulations for geographic areas below the state level. Using these resources, this study: (1) considers in greater detail how the properties of the same-sex couple error might affect statistical inference, (2) offers a method for developing sub-state estimates of same-sex couples, and (3) demonstrates how using adjusted estimates can improve inference in analyses that rely on understanding the distribution of same-sex couples. In order to accomplish the third task, we replicate an analysis by McVeigh and Diaz (American Sociological Review 74: 891–915, 2009) that used county level Census 2000 unadjusted same-sex couple data, substitute our adjusted same-sex couple estimate, and examine the way in which this substitution affects findings. Our results demonstrate the improved accuracy of the adjusted measure and provide the formula that researchers can use to adjust the same-sex couple distribution in future analyses.

Keywords

Demography Measurement Sexuality Quantitative methods Methodology 

Notes

Acknowledgments

We would like to thank Rory McVeigh and Maria-Elena D. Diaz for generously sharing their data for this analysis.

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Department of SociologyUniversity of California, Los AngelesLos AngelesUSA
  2. 2.The Williams InstituteUniversity of California, Los Angeles School of LawLos AngelesUSA

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